st: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation

st: combining/integrating the results of -stepwise- and -mim- for variable selection after multiple imputation

Date

Thu, 9 Sep 2010 14:30:30 -0400

Dear All,
I am using Stata 11.1 and attempting to perform variable selection
after multiple imputation. All 10 imputed datasets are currently
stacked into one large data set with "_mj" identifying the dataset to
which an observation belongs and "_mi" identifying observations within
a data set.
In their paper "How should variable selection be performed with
multiply imputed data?", Wood et al. (2008) identify a model selection
approach (the "RR appoach") that utilizes Rubin's rules for estimating
parameters and standard errors across imputed data sets.
Specifically, "each model selection step involves fitting the model
under consideration to all data sets and combining estimates across
imputed data sets." The only information that they provide in regards
to actually doing this in Stata is the following: "For the RR method,
-stepwise- was modified to use the Wald test statistics from
-micombine- ."
I am only an intermediate Stata user on my best days so I'm not even
really sure where to start on this. It seems like I need to code an
iterative procedure in which the results of each -logistic- command
run under -stepwise- are fed to -micombine (or -mim-) which then
combines the results across the imputed data sets and finally feeds
the resulting Wald test statistic back to -stepwise- in order for the
next -logistic- command to be able to run. Any advice do doing on
setting up this type of program?
This is the web address of the the Wood et al. paper for your
reference: http://onlinelibrary.wiley.com/doi/10.1002/sim.3177/abstract
Unfortunately, access to the full .pdf is only granted if you have a
subscription. I couldn't find a location in which it is available to
everyone.
Thanks for the consideration!
Jordan
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